Overview

Brought to you by YData

Dataset statistics

Number of variables12
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows33
Duplicate rows (%)3.3%
Total size in memory390.1 KiB
Average record size in memory399.5 B

Variable types

Text1
Numeric7
Categorical4

Alerts

Dataset has 33 (3.3%) duplicate rowsDuplicates
engine is highly overall correlated with max_power and 2 other fieldsHigh correlation
km_driven is highly overall correlated with yearHigh correlation
max_power is highly overall correlated with engine and 2 other fieldsHigh correlation
seats is highly overall correlated with engineHigh correlation
selling_price is highly overall correlated with engine and 3 other fieldsHigh correlation
transmission is highly overall correlated with max_power and 1 other fieldsHigh correlation
year is highly overall correlated with km_driven and 1 other fieldsHigh correlation
seller_type is highly imbalanced (52.7%) Imbalance

Reproduction

Analysis started2024-11-27 19:15:04.813060
Analysis finished2024-11-27 19:15:24.361511
Duration19.55 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

name
Text

Distinct621
Distinct (%)62.1%
Missing0
Missing (%)0.0%
Memory size80.1 KiB
2024-11-27T19:15:24.790645image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length49
Median length39
Mean length24.857
Min length11

Characters and Unicode

Total characters24857
Distinct characters67
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique440 ?
Unique (%)44.0%

Sample

1st rowMahindra Xylo E4 BS IV
2nd rowTata Nexon 1.5 Revotorq XE
3rd rowHonda Civic 1.8 S AT
4th rowHonda City i DTEC VX
5th rowTata Indica Vista Aura 1.2 Safire BSIV
ValueCountFrequency (%)
maruti 290
 
6.2%
hyundai 198
 
4.2%
tata 106
 
2.3%
mahindra 90
 
1.9%
swift 83
 
1.8%
diesel 83
 
1.8%
bsiv 79
 
1.7%
vxi 74
 
1.6%
1.2 71
 
1.5%
plus 64
 
1.4%
Other values (495) 3549
75.7%
2024-11-27T19:15:25.717758image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3687
 
14.8%
a 1852
 
7.5%
i 1631
 
6.6%
t 1253
 
5.0%
r 1094
 
4.4%
o 1010
 
4.1%
n 934
 
3.8%
e 890
 
3.6%
u 738
 
3.0%
S 701
 
2.8%
Other values (57) 11067
44.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24857
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3687
 
14.8%
a 1852
 
7.5%
i 1631
 
6.6%
t 1253
 
5.0%
r 1094
 
4.4%
o 1010
 
4.1%
n 934
 
3.8%
e 890
 
3.6%
u 738
 
3.0%
S 701
 
2.8%
Other values (57) 11067
44.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24857
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3687
 
14.8%
a 1852
 
7.5%
i 1631
 
6.6%
t 1253
 
5.0%
r 1094
 
4.4%
o 1010
 
4.1%
n 934
 
3.8%
e 890
 
3.6%
u 738
 
3.0%
S 701
 
2.8%
Other values (57) 11067
44.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24857
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3687
 
14.8%
a 1852
 
7.5%
i 1631
 
6.6%
t 1253
 
5.0%
r 1094
 
4.4%
o 1010
 
4.1%
n 934
 
3.8%
e 890
 
3.6%
u 738
 
3.0%
S 701
 
2.8%
Other values (57) 11067
44.5%

year
Real number (ℝ)

High correlation 

Distinct24
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.681
Minimum1995
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-11-27T19:15:26.053363image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1995
5-th percentile2006
Q12011
median2014
Q32017
95-th percentile2019
Maximum2020
Range25
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.0121486
Coefficient of variation (CV)0.001992445
Kurtosis1.2158841
Mean2013.681
Median Absolute Deviation (MAD)3
Skewness-1.0223557
Sum2013681
Variance16.097336
MonotonicityNot monotonic
2024-11-27T19:15:26.380656image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2017 134
13.4%
2016 106
10.6%
2015 96
9.6%
2018 91
9.1%
2011 85
8.5%
2012 83
8.3%
2014 79
7.9%
2013 76
7.6%
2019 64
6.4%
2010 49
 
4.9%
Other values (14) 137
13.7%
ValueCountFrequency (%)
1995 1
 
0.1%
1998 1
 
0.1%
1999 5
 
0.5%
2000 1
 
0.1%
2001 2
 
0.2%
2002 4
 
0.4%
2003 8
 
0.8%
2004 10
1.0%
2005 10
1.0%
2006 20
2.0%
ValueCountFrequency (%)
2020 4
 
0.4%
2019 64
6.4%
2018 91
9.1%
2017 134
13.4%
2016 106
10.6%
2015 96
9.6%
2014 79
7.9%
2013 76
7.6%
2012 83
8.3%
2011 85
8.5%

selling_price
Real number (ℝ)

High correlation 

Distinct274
Distinct (%)27.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean617901.04
Minimum31000
Maximum6000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-11-27T19:15:26.676013image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum31000
5-th percentile100000
Q1250000
median434999
Q3670000
95-th percentile1904049
Maximum6000000
Range5969000
Interquartile range (IQR)420000

Descriptive statistics

Standard deviation758553.86
Coefficient of variation (CV)1.22763
Kurtosis21.438457
Mean617901.04
Median Absolute Deviation (MAD)205000
Skewness4.2148309
Sum6.1790104 × 108
Variance5.7540396 × 1011
MonotonicityNot monotonic
2024-11-27T19:15:27.023964image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300000 29
 
2.9%
350000 28
 
2.8%
600000 28
 
2.8%
550000 25
 
2.5%
650000 24
 
2.4%
400000 24
 
2.4%
250000 22
 
2.2%
500000 22
 
2.2%
750000 22
 
2.2%
450000 16
 
1.6%
Other values (264) 760
76.0%
ValueCountFrequency (%)
31000 1
 
0.1%
33983 1
 
0.1%
35000 1
 
0.1%
40000 1
 
0.1%
45000 5
0.5%
46000 1
 
0.1%
50000 2
 
0.2%
52000 2
 
0.2%
55000 3
0.3%
55599 1
 
0.1%
ValueCountFrequency (%)
6000000 2
 
0.2%
5500000 5
0.5%
5400000 2
 
0.2%
5150000 3
 
0.3%
4100000 1
 
0.1%
3800000 2
 
0.2%
3750000 1
 
0.1%
3400000 1
 
0.1%
3251000 1
 
0.1%
3200000 8
0.8%

km_driven
Real number (ℝ)

High correlation 

Distinct260
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71393.341
Minimum1303
Maximum375000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-11-27T19:15:27.351855image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1303
5-th percentile9190
Q137000
median61500
Q3100000
95-th percentile160000
Maximum375000
Range373697
Interquartile range (IQR)63000

Descriptive statistics

Standard deviation48486.219
Coefficient of variation (CV)0.67914203
Kurtosis3.8337561
Mean71393.341
Median Absolute Deviation (MAD)28500
Skewness1.4228571
Sum71393341
Variance2.3509134 × 109
MonotonicityNot monotonic
2024-11-27T19:15:27.724325image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120000 66
 
6.6%
70000 58
 
5.8%
60000 55
 
5.5%
80000 54
 
5.4%
40000 46
 
4.6%
50000 44
 
4.4%
90000 38
 
3.8%
110000 35
 
3.5%
100000 33
 
3.3%
30000 27
 
2.7%
Other values (250) 544
54.4%
ValueCountFrequency (%)
1303 1
 
0.1%
2000 7
0.7%
2388 1
 
0.1%
2600 1
 
0.1%
3100 1
 
0.1%
3500 2
 
0.2%
3564 1
 
0.1%
4000 1
 
0.1%
4337 1
 
0.1%
5000 9
0.9%
ValueCountFrequency (%)
375000 1
0.1%
300000 2
0.2%
298000 1
0.1%
291000 1
0.1%
270000 1
0.1%
265000 1
0.1%
264000 1
0.1%
260000 1
0.1%
250000 1
0.1%
248000 1
0.1%

fuel
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size61.6 KiB
Diesel
534 
Petrol
457 
CNG
 
5
LPG
 
4

Length

Max length6
Median length6
Mean length5.973
Min length3

Characters and Unicode

Total characters5973
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDiesel
2nd rowDiesel
3rd rowPetrol
4th rowDiesel
5th rowPetrol

Common Values

ValueCountFrequency (%)
Diesel 534
53.4%
Petrol 457
45.7%
CNG 5
 
0.5%
LPG 4
 
0.4%

Length

2024-11-27T19:15:28.042516image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T19:15:28.278289image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
diesel 534
53.4%
petrol 457
45.7%
cng 5
 
0.5%
lpg 4
 
0.4%

Most occurring characters

ValueCountFrequency (%)
e 1525
25.5%
l 991
16.6%
D 534
 
8.9%
i 534
 
8.9%
s 534
 
8.9%
P 461
 
7.7%
t 457
 
7.7%
r 457
 
7.7%
o 457
 
7.7%
G 9
 
0.2%
Other values (3) 14
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5973
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1525
25.5%
l 991
16.6%
D 534
 
8.9%
i 534
 
8.9%
s 534
 
8.9%
P 461
 
7.7%
t 457
 
7.7%
r 457
 
7.7%
o 457
 
7.7%
G 9
 
0.2%
Other values (3) 14
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5973
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1525
25.5%
l 991
16.6%
D 534
 
8.9%
i 534
 
8.9%
s 534
 
8.9%
P 461
 
7.7%
t 457
 
7.7%
r 457
 
7.7%
o 457
 
7.7%
G 9
 
0.2%
Other values (3) 14
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5973
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1525
25.5%
l 991
16.6%
D 534
 
8.9%
i 534
 
8.9%
s 534
 
8.9%
P 461
 
7.7%
t 457
 
7.7%
r 457
 
7.7%
o 457
 
7.7%
G 9
 
0.2%
Other values (3) 14
 
0.2%

seller_type
Categorical

Imbalance 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size65.2 KiB
Individual
837 
Dealer
135 
Trustmark Dealer
 
28

Length

Max length16
Median length10
Mean length9.628
Min length6

Characters and Unicode

Total characters9628
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIndividual
2nd rowIndividual
3rd rowIndividual
4th rowIndividual
5th rowIndividual

Common Values

ValueCountFrequency (%)
Individual 837
83.7%
Dealer 135
 
13.5%
Trustmark Dealer 28
 
2.8%

Length

2024-11-27T19:15:28.539224image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T19:15:28.761118image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
individual 837
81.4%
dealer 163
 
15.9%
trustmark 28
 
2.7%

Most occurring characters

ValueCountFrequency (%)
d 1674
17.4%
i 1674
17.4%
a 1028
10.7%
l 1000
10.4%
u 865
9.0%
I 837
8.7%
v 837
8.7%
n 837
8.7%
e 326
 
3.4%
r 219
 
2.3%
Other values (7) 331
 
3.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9628
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 1674
17.4%
i 1674
17.4%
a 1028
10.7%
l 1000
10.4%
u 865
9.0%
I 837
8.7%
v 837
8.7%
n 837
8.7%
e 326
 
3.4%
r 219
 
2.3%
Other values (7) 331
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9628
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 1674
17.4%
i 1674
17.4%
a 1028
10.7%
l 1000
10.4%
u 865
9.0%
I 837
8.7%
v 837
8.7%
n 837
8.7%
e 326
 
3.4%
r 219
 
2.3%
Other values (7) 331
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9628
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 1674
17.4%
i 1674
17.4%
a 1028
10.7%
l 1000
10.4%
u 865
9.0%
I 837
8.7%
v 837
8.7%
n 837
8.7%
e 326
 
3.4%
r 219
 
2.3%
Other values (7) 331
 
3.4%

transmission
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size62.0 KiB
Manual
877 
Automatic
123 

Length

Max length9
Median length6
Mean length6.369
Min length6

Characters and Unicode

Total characters6369
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowManual
2nd rowManual
3rd rowAutomatic
4th rowManual
5th rowManual

Common Values

ValueCountFrequency (%)
Manual 877
87.7%
Automatic 123
 
12.3%

Length

2024-11-27T19:15:29.014354image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T19:15:29.233245image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
manual 877
87.7%
automatic 123
 
12.3%

Most occurring characters

ValueCountFrequency (%)
a 1877
29.5%
u 1000
15.7%
M 877
13.8%
n 877
13.8%
l 877
13.8%
t 246
 
3.9%
A 123
 
1.9%
o 123
 
1.9%
m 123
 
1.9%
i 123
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6369
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1877
29.5%
u 1000
15.7%
M 877
13.8%
n 877
13.8%
l 877
13.8%
t 246
 
3.9%
A 123
 
1.9%
o 123
 
1.9%
m 123
 
1.9%
i 123
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6369
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1877
29.5%
u 1000
15.7%
M 877
13.8%
n 877
13.8%
l 877
13.8%
t 246
 
3.9%
A 123
 
1.9%
o 123
 
1.9%
m 123
 
1.9%
i 123
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6369
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1877
29.5%
u 1000
15.7%
M 877
13.8%
n 877
13.8%
l 877
13.8%
t 246
 
3.9%
A 123
 
1.9%
o 123
 
1.9%
m 123
 
1.9%
i 123
 
1.9%

owner
Categorical

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size67.0 KiB
First Owner
623 
Second Owner
278 
Third Owner
71 
Fourth & Above Owner
 
27
Test Drive Car
 
1

Length

Max length20
Median length11
Mean length11.524
Min length11

Characters and Unicode

Total characters11524
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowFirst Owner
2nd rowFirst Owner
3rd rowFirst Owner
4th rowFirst Owner
5th rowSecond Owner

Common Values

ValueCountFrequency (%)
First Owner 623
62.3%
Second Owner 278
27.8%
Third Owner 71
 
7.1%
Fourth & Above Owner 27
 
2.7%
Test Drive Car 1
 
0.1%

Length

2024-11-27T19:15:29.501369image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T19:15:29.727710image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
owner 999
48.6%
first 623
30.3%
second 278
 
13.5%
third 71
 
3.5%
fourth 27
 
1.3%
27
 
1.3%
above 27
 
1.3%
test 1
 
< 0.1%
drive 1
 
< 0.1%
car 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
r 1722
14.9%
e 1306
11.3%
n 1277
11.1%
1055
9.2%
O 999
8.7%
w 999
8.7%
i 695
6.0%
t 651
 
5.6%
F 650
 
5.6%
s 624
 
5.4%
Other values (14) 1546
13.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11524
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 1722
14.9%
e 1306
11.3%
n 1277
11.1%
1055
9.2%
O 999
8.7%
w 999
8.7%
i 695
6.0%
t 651
 
5.6%
F 650
 
5.6%
s 624
 
5.4%
Other values (14) 1546
13.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11524
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 1722
14.9%
e 1306
11.3%
n 1277
11.1%
1055
9.2%
O 999
8.7%
w 999
8.7%
i 695
6.0%
t 651
 
5.6%
F 650
 
5.6%
s 624
 
5.4%
Other values (14) 1546
13.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11524
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 1722
14.9%
e 1306
11.3%
n 1277
11.1%
1055
9.2%
O 999
8.7%
w 999
8.7%
i 695
6.0%
t 651
 
5.6%
F 650
 
5.6%
s 624
 
5.4%
Other values (14) 1546
13.4%

mileage
Real number (ℝ)

Distinct233
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.33748
Minimum0
Maximum32.26
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-11-27T19:15:30.021227image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12.8
Q116.55
median19.3
Q322.3
95-th percentile25.5
Maximum32.26
Range32.26
Interquartile range (IQR)5.75

Descriptive statistics

Standard deviation3.9517511
Coefficient of variation (CV)0.20435709
Kurtosis0.003254609
Mean19.33748
Median Absolute Deviation (MAD)2.8
Skewness-0.10970283
Sum19337.48
Variance15.616337
MonotonicityNot monotonic
2024-11-27T19:15:30.349291image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.3 26
 
2.6%
18.6 23
 
2.3%
21.1 22
 
2.2%
18.9 22
 
2.2%
19.7 21
 
2.1%
16.1 17
 
1.7%
17 16
 
1.6%
12.8 16
 
1.6%
22.74 15
 
1.5%
18.2 15
 
1.5%
Other values (223) 807
80.7%
ValueCountFrequency (%)
0 1
 
0.1%
9.5 1
 
0.1%
10.5 3
0.3%
10.75 1
 
0.1%
10.91 2
0.2%
10.93 1
 
0.1%
11 1
 
0.1%
11.18 1
 
0.1%
11.2 2
0.2%
11.36 2
0.2%
ValueCountFrequency (%)
32.26 1
 
0.1%
28.4 11
1.1%
28.09 2
 
0.2%
27.39 5
0.5%
27.3 3
 
0.3%
27.28 2
 
0.2%
26.6 1
 
0.1%
26.59 5
0.5%
26.21 2
 
0.2%
26 10
1.0%

engine
Real number (ℝ)

High correlation 

Distinct88
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1454.876
Minimum624
Maximum3604
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-11-27T19:15:30.669595image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum624
5-th percentile796
Q11197
median1248
Q31582
95-th percentile2523
Maximum3604
Range2980
Interquartile range (IQR)385

Descriptive statistics

Standard deviation521.99574
Coefficient of variation (CV)0.35879054
Kurtosis0.90097598
Mean1454.876
Median Absolute Deviation (MAD)248
Skewness1.1890629
Sum1454876
Variance272479.55
MonotonicityNot monotonic
2024-11-27T19:15:31.004790image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1248 135
 
13.5%
1197 105
 
10.5%
796 63
 
6.3%
998 57
 
5.7%
1396 51
 
5.1%
2179 49
 
4.9%
1498 47
 
4.7%
2494 32
 
3.2%
1199 31
 
3.1%
1497 23
 
2.3%
Other values (78) 407
40.7%
ValueCountFrequency (%)
624 7
 
0.7%
796 63
6.3%
799 11
 
1.1%
814 18
 
1.8%
909 1
 
0.1%
936 5
 
0.5%
993 2
 
0.2%
995 2
 
0.2%
998 57
5.7%
999 7
 
0.7%
ValueCountFrequency (%)
3604 1
 
0.1%
3198 2
 
0.2%
2993 3
0.3%
2987 2
 
0.2%
2982 5
0.5%
2956 6
0.6%
2953 1
 
0.1%
2835 1
 
0.1%
2755 5
0.5%
2696 1
 
0.1%

max_power
Real number (ℝ)

High correlation 

Distinct180
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90.84433
Minimum34.2
Maximum280
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-11-27T19:15:31.317388image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum34.2
5-th percentile47.3
Q169
median82.425
Q3102
95-th percentile163.94
Maximum280
Range245.8
Interquartile range (IQR)33

Descriptive statistics

Standard deviation34.892709
Coefficient of variation (CV)0.38409341
Kurtosis3.7253344
Mean90.84433
Median Absolute Deviation (MAD)15.385
Skewness1.5940154
Sum90844.33
Variance1217.5011
MonotonicityNot monotonic
2024-11-27T19:15:31.659350image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
74 43
 
4.3%
88.5 29
 
2.9%
82 29
 
2.9%
47.3 24
 
2.4%
81.8 24
 
2.4%
67.1 22
 
2.2%
46.3 21
 
2.1%
88.7 20
 
2.0%
88.73 20
 
2.0%
70 19
 
1.9%
Other values (170) 749
74.9%
ValueCountFrequency (%)
34.2 2
 
0.2%
35 5
 
0.5%
37 15
1.5%
37.48 2
 
0.2%
38 1
 
0.1%
45 1
 
0.1%
46.3 21
2.1%
47.3 24
2.4%
52 1
 
0.1%
52.8 4
 
0.4%
ValueCountFrequency (%)
280 1
 
0.1%
270.9 1
 
0.1%
254.79 2
 
0.2%
241 1
 
0.1%
235 2
 
0.2%
214.56 3
 
0.3%
204 1
 
0.1%
197 2
 
0.2%
190 9
0.9%
187.74 1
 
0.1%

seats
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.403
Minimum4
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-11-27T19:15:31.911789image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile5
Q15
median5
Q35
95-th percentile7
Maximum9
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.91292082
Coefficient of variation (CV)0.16896554
Kurtosis1.890972
Mean5.403
Median Absolute Deviation (MAD)0
Skewness1.6728153
Sum5403
Variance0.83342442
MonotonicityNot monotonic
2024-11-27T19:15:32.150075image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 777
77.7%
7 161
 
16.1%
4 24
 
2.4%
8 23
 
2.3%
6 8
 
0.8%
9 7
 
0.7%
ValueCountFrequency (%)
4 24
 
2.4%
5 777
77.7%
6 8
 
0.8%
7 161
 
16.1%
8 23
 
2.3%
9 7
 
0.7%
ValueCountFrequency (%)
9 7
 
0.7%
8 23
 
2.3%
7 161
 
16.1%
6 8
 
0.8%
5 777
77.7%
4 24
 
2.4%

Interactions

2024-11-27T19:15:21.389131image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:15:06.545040image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:15:09.891106image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:15:12.284918image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:15:15.054276image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:15:17.178898image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:15:19.044484image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:15:21.731012image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:15:07.134583image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:15:10.235112image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:15:12.812771image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:15:15.481932image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:15:17.444236image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:15:19.328567image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:15:22.080293image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:15:07.785469image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:15:10.561947image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:15:13.138436image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:15:15.927673image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:15:17.683506image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:15:19.647751image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:15:22.458212image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:15:08.441483image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:15:10.928874image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:15:13.557106image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:15:16.192475image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:15:17.931979image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:15:19.977372image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:15:22.808916image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:15:08.929126image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:15:11.255029image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:15:13.969028image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:15:16.441158image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:15:18.200665image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:15:20.337456image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:15:23.120295image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:15:09.300915image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:15:11.553871image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:15:14.297389image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:15:16.688836image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:15:18.447735image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:15:20.704695image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:15:23.389200image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:15:09.605005image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:15:11.941950image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:15:14.642818image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:15:16.943247image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:15:18.680692image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:15:21.040174image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-11-27T19:15:32.345273image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
enginefuelkm_drivenmax_powermileageownerseatsseller_typeselling_pricetransmissionyear
engine1.0000.4380.2610.772-0.4630.0400.5430.2490.5160.4990.015
fuel0.4381.0000.1740.2380.3050.0000.2200.1060.1500.0000.133
km_driven0.2610.1741.0000.048-0.2020.1640.2360.142-0.3280.243-0.597
max_power0.7720.2380.0481.000-0.3480.0670.3410.2550.6660.5820.211
mileage-0.4630.305-0.202-0.3481.0000.080-0.4450.090-0.0190.2210.316
owner0.0400.0000.1640.0670.0801.0000.0600.1740.1650.1470.281
seats0.5430.2200.2360.341-0.4450.0601.0000.0250.2970.0340.027
seller_type0.2490.1060.1420.2550.0900.1740.0251.0000.3640.3620.196
selling_price0.5160.150-0.3280.666-0.0190.1650.2970.3641.0000.6280.710
transmission0.4990.0000.2430.5820.2210.1470.0340.3620.6281.0000.308
year0.0150.133-0.5970.2110.3160.2810.0270.1960.7100.3081.000

Missing values

2024-11-27T19:15:23.722705image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-27T19:15:24.144781image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

nameyearselling_pricekm_drivenfuelseller_typetransmissionownermileageenginemax_powerseats
0Mahindra Xylo E4 BS IV2010229999168000DieselIndividualManualFirst Owner14.002498112.07
1Tata Nexon 1.5 Revotorq XE201766500025000DieselIndividualManualFirst Owner21.501497108.55
2Honda Civic 1.8 S AT2007175000218463PetrolIndividualAutomaticFirst Owner12.901799130.05
3Honda City i DTEC VX2015635000173000DieselIndividualManualFirst Owner25.10149898.65
4Tata Indica Vista Aura 1.2 Safire BSIV201113000070000PetrolIndividualManualSecond Owner16.50117265.05
5Mahindra Thar CRDe201997500012584DieselDealerManualFirst Owner16.552498105.06
6Chevrolet Spark 1.0 LS201115000035000PetrolIndividualManualFirst Owner18.0099562.05
7Maruti Ritz ZXi201227500070000PetrolIndividualManualSecond Owner18.50119785.85
8Maruti Alto LX201114000072000PetrolIndividualManualSecond Owner19.7079646.35
9Hyundai Creta 1.6 CRDi SX201685000058000DieselIndividualManualFirst Owner19.671582126.25
nameyearselling_pricekm_drivenfuelseller_typetransmissionownermileageenginemax_powerseats
990Maruti Alto LXi20079500070000PetrolIndividualManualSecond Owner19.7079646.305
991Honda Brio V MT201237600026000PetrolIndividualManualFirst Owner19.40119886.805
992Maruti Alto LXi200685000150000PetrolIndividualManualSecond Owner19.7079646.305
993Maruti 800 DX199952000100000PetrolIndividualManualFirst Owner16.1079637.004
994Maruti Swift Dzire VXi2010240000143000PetrolIndividualManualFirst Owner17.50129885.805
995Hyundai i10 Magna 1.1L2008250000100000PetrolIndividualManualSecond Owner19.81108668.055
996Hyundai i20 2015-2017 Sportz 1.2201744000050000PetrolIndividualManualSecond Owner18.60119781.835
997Hyundai i20 Era Diesel200934000040000DieselIndividualManualFirst Owner23.00139690.005
998Hyundai i10 Asta201235000025000PetrolIndividualManualFirst Owner20.36119778.905
999Honda City i DTec SV2016700000110000DieselIndividualManualFirst Owner26.00149898.605

Duplicate rows

Most frequently occurring

nameyearselling_pricekm_drivenfuelseller_typetransmissionownermileageenginemax_powerseats# duplicates
2Honda Jazz VX201655000056494PetrolTrustmark DealerManualFirst Owner18.20119988.7058
9Jaguar XF 2.0 Diesel Portfolio2017320000045000DieselDealerAutomaticFirst Owner19.331999177.0056
28Toyota Camry 2.5 Hybrid2016200000068089PetrolTrustmark DealerAutomaticFirst Owner19.162494157.7056
31Volvo V40 D3 R-Design201824750002000DieselDealerAutomaticFirst Owner16.801984150.0056
1BMW X4 M Sport X xDrive20d201955000008500DieselDealerAutomaticFirst Owner16.781995190.0054
17Maruti Swift AMT VVT VXI20196500005621PetrolTrustmark DealerAutomaticFirst Owner22.00119781.8054
23Skoda Rapid 1.6 MPI AT Elegance201664500011000PetrolDealerAutomaticFirst Owner14.301598103.5054
25Tata Safari Storme EX2015503000110000DieselIndividualManualFirst Owner14.102179147.9474
4Hyundai Grand i10 1.2 CRDi Sportz201745000056290DieselDealerManualFirst Owner24.00118673.9753
10Lexus ES 300h2019515000020000PetrolDealerAutomaticFirst Owner22.372487214.5653